Research Projects A-Z

11/5/18
Extending the work that was completed for year one funding related to “Developing Highway Safety Performance Metrics in an Advanced Connected Vehicle Environment Utilizing Near-Crash Events from the SHRP 2 Naturalistic Driving Study.”
11/5/18
Investigating how multiple traffic data sources can be integrated in a consistent manner, and how they may be best used for arterial performance measurement.
11/2/17
Developing models that will predict the delay a passenger car or a truck is likely to encounter by the time the vehicle arrives at the border.
1/12/18
Designing a transportation data-warehouse prototype for the Buffalo-Niagara region and demonstrating its usefulness through a specific application.
12/4/17
This project develops novel data mining methodologies that integrate heterogeneous urban data for the estimation of city-wide transportation information.
11/2/17
Pooling P3 project data from various sources to build a database that can be analyzed and used to inform future decision making.
11/2/17
Developing a smartphone-based travel behavior data collection platform that recruits participants by rewarding users with real-time parking information.
11/2/17
Analyzing traffic violations and traffic crash records to develop a probabilistic model that will help detect high risk drivers with the main goal of preventing future crashes.
11/2/17
Conducting a detailed, multivariate statistical assessment of pavement treatments by public-private partnerships, and studying their performance in terms of extending pavement lives.
11/27/17
Invistigating aggressive driving behavior under driver fatigue, and under normal and distracted driving conditions.
11/22/17
Developing a novel green navigation system, called Green Nav, that gives a driver the most fuel efficient route for his vehicle as opposed to the shortest or fastest route.
11/10/17
Examining in-vehicle and infrastructure-based technologies to assess how they might impact emergency responders, particularly EMS.
11/5/18
Developing a predictive statistical framework to efficiently estimate the ability of a bike-sharing system to serve incoming bike requests.
11/2/17
Exploring historical incident and traffic data to revolutionize response strategies.
11/2/17
Understanding and expressing public transit system utilization based on fundamental travel behavior.
12/4/17
Creating a mobile computer application for documenting and sharing data regarding vehicular accidents.
11/2/17
Developing the tools needed to process immense amounts of data, develop new performance metrics based on the data collected, and propose methods to enhance performance.
11/2/17
Exploring the potential for using a number of machine learning and data mining methods to analyze accident data.
11/17/17
The project investigates how real-time conditions interact to affect driver safety performance changes. From that understanding, practitioners and drivers can make more informed decisions to reduce the likelihood of a crash.
11/20/17
Incorporating data analytics into paratransit planning and operations is a promising approach for increasing their cost-effectiveness.
12/1/17
Creating a quality-aware crowdsourced road sensing system that integrates sensory data from multiple vehicles while placing more weight on the vehicles that provide high quality data to significantly improve integration accuracy.
11/20/17
The project proposes a deep learning model to predict the best recharging recommendation including best recharging time and location for eTaxi drivers.
11/2/17
Mining social media data to deduce useful information about present or future travelers’ behavior, with a special emphasis under events, including both planned and unplanned.
11/17/17
The project suggests a bottom-up travel behavior driven approach which obtains trends in individual travel behavior first and use such information to enhance longitudinal origin-destination demand monitoring.
11/5/18
Integrating machine learning, big data, sensor networks, and agent-based transportation modeling to prototype an algorithm that combines the power of a model-driven approach with the power of big data.